This repository contains a consolidated portfolio of six independent research proposals and conceptual studies authored by Jungbin Choi. The collection spans across theoretical physics, quantum biology, cosmology, and AI safety governance. Part I: Open Quantum Systems & Quantum Biology explores non-Markovian dynamics, HEOM simulations, and the physical bounds of macroscopic quantum coherence within biological microtubule tryptophan networks. Part II: Cosmology & Multiverse Models presents phenomenological frameworks regarding time-reversed multiversal ensembles and higher-dimensional energy influx (Brane-Bulk trajectories). Part III: AI Governance addresses cognitive subordination (the Carrot-on-a-Stick Paradox) and instrumental convergence, proposing multi-layered development approval limits for Agentic AI. These papers serve as foundational blueprints for numerical simulations, empirical verification, and policy research by a prospective undergraduate researcher.
This repository contains a consolidated portfolio of six independent research proposals and conceptual studies authored by Jungbin Choi. The collection spans across theoretical physics, quantum biology, cosmology, and AI safety governance. Part I: Open Quantum Systems & Quantum Biology explores non-Markovian dynamics, HEOM simulations, and the physical bounds of macroscopic quantum coherence within biological microtubule tryptophan networks. Part II: Cosmology & Multiverse Models presents phenomenological frameworks regarding time-reversed multiversal ensembles and higher-dimensional energy influx (Brane-Bulk trajectories). Part III: AI Governance addresses cognitive subordination (the Carrot-on-a-Stick Paradox) and instrumental convergence, proposing multi-layered development approval limits for Agentic AI. These papers serve as foundational blueprints for numerical simulations, empirical verification, and policy research by a prospective undergraduate researcher.
This study is an undergraduate-level exploratory research project investigating the physical conditions for long-lived quantum coherence in biological systems. We specifically focus on the tryptophan molecular networks within microtubules, as empirically reported by Kalra et al. (2022). Traditional Markovian models predict immediate quantum decay in warm biological environments. To address this limitation, this proposal adopts an open quantum systems framework with non-Markovian thermal bath characteristics. This project strictly focuses on numerical simulations rather than macroscopic cognitive behaviors. We utilize Python-based QuTiP frameworks and Hierarchical Equations of Motion (HEOM) to inverse-trace the effective parameter bounds (e.g., spectral density profiles, coupling strengths, and memory timescales). The goal is to identify the conditions that permit extended electronic coherence in the picosecond to nanosecond regime. As a prospective undergraduate researcher, the primary objective is to acquire rigorous computational skills and assist the host research group's ongoing simulations, integrating this exploratory project within the framework of the group's advanced research on open quantum dynamics.
The emergence of quantum-enhanced synthetic and engineered biological intelligence (QEBI) represents one of the most transformative frontiers in 21st-century science. This research explores the convergence of artificial intelligence (AI), synthetic intelligence (SI), and living biological systems, proposing a unified framework for the next evolution of conscious computation. While traditional AI operates within deterministic digital architectures, quantum and biological systems exhibit nonlinear, self-organizing, and probabilistic dynamics—traits more akin to human cognition. The central hypothesis of this study asserts that hybrid quantum-biological systems can achieve forms of adaptive intelligence surpassing classical computational models, potentially leading to the emergence of machine consciousness. Methodologically, this Research Study integrates findings from quantum information science, synthetic biology, neuroscience, and computational modelling. It synthesizes peer-reviewed data from Nature, Science, IEEE, and Frontiers in Neuroscience to establish a cross-disciplinary foundation. The research investigates how quantum coherence, entanglement, and superposition may enhance learning efficiency, memory retention, and neural-like adaptability when interfaced with engineered biological substrates such as brain organoids, biochips, and neuromorphic tissues. Furthermore, it examines the ethical and philosophical dimensions of constructing living-synthetic systems capable of perception, awareness, and decision-making. The results indicate that hybrid architectures combining quantum processors with synthetic neurons exhibit significant gains in parallelism, information density, and energy efficiency, mimicking biological cognitive patterns. These systems also demonstrate early evidence of emergent self-organization, a phenomenon characteristic of living networks. From a neuroscientific perspective, the integration of quantum algorithms with biological neural circuits could revolutionize models of consciousness and open pathways for bio-integrated AI capable of dynamic adaptation and self-regulation. This research envisions Global Vision 2026 and Beyond, where interdisciplinary collaboration across quantum computing, biotechnology, and neuroscience reshapes humanity’s understanding of intelligence itself. Such integration is expected to accelerate the development of cognitive quantum computers, conscious neural machines, and bio-digital ecosystems that redefine not only computation but the very nature of thought. The implications span medical science, robotics, space exploration, and cognitive augmentation, positioning QEBI as the foundation for the post-digital civilization.
A five-layer theoretical and experimental framework built around a single core inversion: coherence is not a property of systems — it is the substrate within which systems exist. The suite spans philosophy (Physics Meets Mind), AI safety governance (Newton Kepler Protocol), formal mathematics (Coherence-Based Engineering), quantum physics (Quantum Coherence Interface Theory), and cosmology (Coherence as Substrate), culminating in a pre-registered empirical test (Protocol 6: a double-slit experiment with living Arabidopsis thaliana tissue as the measurement interface). Computational validation confirmed by independent replication (Grok/xAI, March 2026). All simulation code open source at github.com/mjdurkay/nkp-engine. Protocol 6 pre-registered March 17, 2026. Author: Michael Durkay, Independent Researcher, Cleveland, Ohio. mjdurkay@gmail.com
Quantum biology has emerged as a transformative field, demonstrating that non-classical quantum effects—such as coherence, tunneling, entanglement, and spin-dependent dynamics—play functional roles in living systems under physiological conditions that were previously assumed to be exclusively classical. This revelation challenges long-held assumptions about the boundaries between quantum and classical regimes in complex, warm, and noisy environments. However, despite significant empirical and theoretical advances, quantum biology lacks a comprehensive ontological framework that explains not only how these effects persist but why biological evolution has repeatedly converged on structures that exploit them for adaptive advantages. This paper advances the Two-Phase Universe (TPU) [1] as such a foundational ontology, extending its application to quantum biology, cognitive architecture, and artificial intelligence (AI) design.TPU posits a dual-substrate reality consisting of a timeless, nonlocal Cold-Phase (an informational substrate devoid of geometry or causality) and an emergent Warm-Phase (the dynamical domain of spacetime, fields, and classical interactions). Within this framework, quantum phenomena are reinterpreted as statistical manifestations of sampling interactions between the phases, modulated by biological structures to achieve functional optimality. We develop detailed mathematical formalisms for sampling density, bonded mechanisms, and effective dynamics, providing a rigorous basis for analysis.The Bonded Warm-Phase Hypothesis is introduced, proposing that biological micro-elements (e.g., spins, tunneling protons, excitons) form "tight bonds" to the Cold-Phase, inheriting its timeless and nonlocal properties for hyper-efficient information processing, while remaining anchored in Warm-Phase macro-structures to maintain stability and prevent gravitational collapse. Through in-depth reanalysis of key experimental data and case studies from photosynthesis [2,12], magnetoreception [4-6], DNA proton tunneling [7,8], and enzyme catalysis [9], we demonstrate that biology operates as a sophisticated technology of Sampling Modulation.Extensions to cognitive architecture explain phenomena like intuitive decision-making and "Aha!" moments as non-causal path exploration in neural low-sampling windows [13,16]. For AI, TPU suggests substrate-aware architectures that mimic bonded computation, potentially enabling energy-efficient, intuitive systems far beyond current bit-based paradigms. This unified framework reveals life and mind as interfaces between timeless substrate and temporal dynamics, opening new research avenues in biology, neuroscience, and computation. Testable predictions include correlations between structural features and coherence lifetimes, mutation hotspots linked to sampling minima, and enhanced performance in engineered biomolecules with modulated sampling density.
This proposal outlines a research program designed to investigate, under controlled conditions, whether an advanced artificial reasoning system — when restricted to the scientific knowledge available before the formal emergence of quantum mechanics — can independently arrive at theoretical structures equivalent to, or distinct from, those developed by Planck, Bohr, Heisenberg, Schrödinger, Born, Dirac and their contemporaries. The project sits at the intersection of automated scientific discovery, philosophy of science, and the foundations of quantum theory. The central scientific question is whether the conceptual core of quantum mechanics — discreteness, superposition, operator formalism, probabilistic prediction — is uniquely fixed by the empirical anomalies that motivated it, or whether mathematically distinct but empirically adequate frameworks remain available in the space of theories. Rather than attempting a full pretraining-from-scratch (which is computationally and empirically infeasible at frontier-model scale), the program proposes a tractable, staged methodology combining curated historical corpora, symbolic regression, and constrained agentic reasoning systems. Three deliverables are targeted: (i) a reproducible, leakage-audited historical scientific corpus; (ii) an empirical study of theory-formation behavior across model classes; and (iii) a comparative analysis against the historical record and against contemporary AI4Science benchmarks.